This study has achieved significant advancements in predicting sepsis outcomes by utilizing advanced machine learning techniques and sophisticated data preprocessing methods.

These methods include data grouping and effective solutions to data imbalance issues found in the MIMIC-IV database.

Remarkably, our approach is characterized by its efficiency, relying on a limited number of features to generate highly accurate predictions, as indicated by a robust AUROC score and enhanced stability, which is reflected in a narrower confidence interval.

As the number of variables decreased, the model became more stable compared to the results in the literature, which used many more features.

Second, the AUROC for this study is higher compared to other sepsis mortality prediction papers.

From a real-life perspective, fewer features are more interpretable, which can help doctors and clinicians focus on the features that are more related to sepsis mortality.

For the critical task of interpreting feature importance, we have incorporated the SHAP analysis, known for its consistency and the ability to provide a detailed explanation that is comprehensible to audiences from varied backgrounds.

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